Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations920
Missing cells1,759
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory115.1 KiB
Average record size in memory128.1 B

Variable types

Numeric6
Categorical8
Boolean2

Alerts

dataset is highly overall correlated with idHigh correlation
id is highly overall correlated with datasetHigh correlation
trestbps has 59 (6.4%) missing valuesMissing
chol has 30 (3.3%) missing valuesMissing
fbs has 90 (9.8%) missing valuesMissing
thalch has 55 (6.0%) missing valuesMissing
exang has 55 (6.0%) missing valuesMissing
oldpeak has 62 (6.7%) missing valuesMissing
slope has 309 (33.6%) missing valuesMissing
ca has 611 (66.4%) missing valuesMissing
thal has 486 (52.8%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
chol has 172 (18.7%) zerosZeros
oldpeak has 370 (40.2%) zerosZeros

Reproduction

Analysis started2025-10-24 09:52:01.263394
Analysis finished2025-10-24 09:52:10.869422
Duration9.61 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct920
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460.5
Minimum1
Maximum920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-10-24T15:52:11.035496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46.95
Q1230.75
median460.5
Q3690.25
95-th percentile874.05
Maximum920
Range919
Interquartile range (IQR)459.5

Descriptive statistics

Standard deviation265.72542
Coefficient of variation (CV)0.57703675
Kurtosis-1.2
Mean460.5
Median Absolute Deviation (MAD)230
Skewness0
Sum423660
Variance70610
MonotonicityStrictly increasing
2025-10-24T15:52:11.314900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9201
 
0.1%
11
 
0.1%
21
 
0.1%
31
 
0.1%
41
 
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
9041
 
0.1%
9031
 
0.1%
Other values (910)910
98.9%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
9201
0.1%
9191
0.1%
9181
0.1%
9171
0.1%
9161
0.1%
9151
0.1%
9141
0.1%
9131
0.1%
9121
0.1%
9111
0.1%

age
Real number (ℝ)

Distinct50
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.51087
Minimum28
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-10-24T15:52:11.573806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q147
median54
Q360
95-th percentile68
Maximum77
Range49
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4246852
Coefficient of variation (CV)0.17612656
Kurtosis-0.38292982
Mean53.51087
Median Absolute Deviation (MAD)6.5
Skewness-0.19599386
Sum49230
Variance88.824691
MonotonicityNot monotonic
2025-10-24T15:52:11.836046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5451
 
5.5%
5843
 
4.7%
5541
 
4.5%
5638
 
4.1%
5738
 
4.1%
5236
 
3.9%
5935
 
3.8%
5135
 
3.8%
6235
 
3.8%
5333
 
3.6%
Other values (40)535
58.2%
ValueCountFrequency (%)
281
 
0.1%
293
 
0.3%
301
 
0.1%
312
 
0.2%
325
0.5%
332
 
0.2%
347
0.8%
3511
1.2%
366
0.7%
3711
1.2%
ValueCountFrequency (%)
772
 
0.2%
762
 
0.2%
753
 
0.3%
747
0.8%
731
 
0.1%
724
 
0.4%
715
 
0.5%
707
0.8%
6913
1.4%
6810
1.1%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
Male
726 
Female
194 

Length

Max length6
Median length4
Mean length4.4217391
Min length4

Characters and Unicode

Total characters4,068
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male726
78.9%
Female194
 
21.1%

Length

2025-10-24T15:52:12.111707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T15:52:12.333157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
male726
78.9%
female194
 
21.1%

Most occurring characters

ValueCountFrequency (%)
e1114
27.4%
a920
22.6%
l920
22.6%
M726
17.8%
F194
 
4.8%
m194
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1114
27.4%
a920
22.6%
l920
22.6%
M726
17.8%
F194
 
4.8%
m194
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1114
27.4%
a920
22.6%
l920
22.6%
M726
17.8%
F194
 
4.8%
m194
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1114
27.4%
a920
22.6%
l920
22.6%
M726
17.8%
F194
 
4.8%
m194
 
4.8%

dataset
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
Cleveland
304 
Hungary
293 
VA Long Beach
200 
Switzerland
123 

Length

Max length13
Median length11
Mean length9.5
Min length7

Characters and Unicode

Total characters8,740
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCleveland
2nd rowCleveland
3rd rowCleveland
4th rowCleveland
5th rowCleveland

Common Values

ValueCountFrequency (%)
Cleveland304
33.0%
Hungary293
31.8%
VA Long Beach200
21.7%
Switzerland123
13.4%

Length

2025-10-24T15:52:12.545592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T15:52:12.748568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
cleveland304
23.0%
hungary293
22.2%
va200
15.2%
long200
15.2%
beach200
15.2%
switzerland123
9.3%

Most occurring characters

ValueCountFrequency (%)
e931
 
10.7%
n920
 
10.5%
a920
 
10.5%
l731
 
8.4%
g493
 
5.6%
d427
 
4.9%
r416
 
4.8%
400
 
4.6%
C304
 
3.5%
v304
 
3.5%
Other values (15)2894
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)8740
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e931
 
10.7%
n920
 
10.5%
a920
 
10.5%
l731
 
8.4%
g493
 
5.6%
d427
 
4.9%
r416
 
4.8%
400
 
4.6%
C304
 
3.5%
v304
 
3.5%
Other values (15)2894
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8740
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e931
 
10.7%
n920
 
10.5%
a920
 
10.5%
l731
 
8.4%
g493
 
5.6%
d427
 
4.9%
r416
 
4.8%
400
 
4.6%
C304
 
3.5%
v304
 
3.5%
Other values (15)2894
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8740
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e931
 
10.7%
n920
 
10.5%
a920
 
10.5%
l731
 
8.4%
g493
 
5.6%
d427
 
4.9%
r416
 
4.8%
400
 
4.6%
C304
 
3.5%
v304
 
3.5%
Other values (15)2894
33.1%

cp
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
asymptomatic
496 
non-anginal
204 
atypical angina
174 
typical angina
 
46

Length

Max length15
Median length12
Mean length12.445652
Min length11

Characters and Unicode

Total characters11,450
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtypical angina
2nd rowasymptomatic
3rd rowasymptomatic
4th rownon-anginal
5th rowatypical angina

Common Values

ValueCountFrequency (%)
asymptomatic496
53.9%
non-anginal204
22.2%
atypical angina174
 
18.9%
typical angina46
 
5.0%

Length

2025-10-24T15:52:12.995119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T15:52:13.214305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
asymptomatic496
43.5%
angina220
19.3%
non-anginal204
17.9%
atypical174
 
15.3%
typical46
 
4.0%

Most occurring characters

ValueCountFrequency (%)
a2234
19.5%
n1256
11.0%
t1212
10.6%
i1140
10.0%
m992
8.7%
y716
 
6.3%
c716
 
6.3%
p716
 
6.3%
o700
 
6.1%
s496
 
4.3%
Other values (4)1272
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)11450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2234
19.5%
n1256
11.0%
t1212
10.6%
i1140
10.0%
m992
8.7%
y716
 
6.3%
c716
 
6.3%
p716
 
6.3%
o700
 
6.1%
s496
 
4.3%
Other values (4)1272
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2234
19.5%
n1256
11.0%
t1212
10.6%
i1140
10.0%
m992
8.7%
y716
 
6.3%
c716
 
6.3%
p716
 
6.3%
o700
 
6.1%
s496
 
4.3%
Other values (4)1272
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2234
19.5%
n1256
11.0%
t1212
10.6%
i1140
10.0%
m992
8.7%
y716
 
6.3%
c716
 
6.3%
p716
 
6.3%
o700
 
6.1%
s496
 
4.3%
Other values (4)1272
11.1%

trestbps
Real number (ℝ)

Missing 

Distinct61
Distinct (%)7.1%
Missing59
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean132.1324
Minimum0
Maximum200
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-10-24T15:52:13.454351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range200
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.06607
Coefficient of variation (CV)0.14429518
Kurtosis2.9586644
Mean132.1324
Median Absolute Deviation (MAD)10
Skewness0.21333447
Sum113766
Variance363.51501
MonotonicityNot monotonic
2025-10-24T15:52:13.750408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120131
14.2%
130115
12.5%
140102
 
11.1%
11059
 
6.4%
15056
 
6.1%
16050
 
5.4%
12529
 
3.2%
11519
 
2.1%
13518
 
2.0%
12817
 
1.8%
Other values (51)265
28.8%
(Missing)59
 
6.4%
ValueCountFrequency (%)
01
 
0.1%
801
 
0.1%
921
 
0.1%
942
 
0.2%
956
 
0.7%
961
 
0.1%
981
 
0.1%
10015
1.6%
1011
 
0.1%
1023
 
0.3%
ValueCountFrequency (%)
2004
 
0.4%
1921
 
0.1%
1902
 
0.2%
1851
 
0.1%
18012
1.3%
1783
 
0.3%
1741
 
0.1%
1722
 
0.2%
17014
1.5%
1652
 
0.2%

chol
Real number (ℝ)

Missing  Zeros 

Distinct217
Distinct (%)24.4%
Missing30
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean199.13034
Minimum0
Maximum603
Zeros172
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-10-24T15:52:14.393430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1175
median223
Q3268
95-th percentile334.1
Maximum603
Range603
Interquartile range (IQR)93

Descriptive statistics

Standard deviation110.78081
Coefficient of variation (CV)0.55632312
Kurtosis0.062272688
Mean199.13034
Median Absolute Deviation (MAD)46
Skewness-0.61383609
Sum177226
Variance12272.388
MonotonicityNot monotonic
2025-10-24T15:52:14.663757image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
 
18.7%
25410
 
1.1%
22010
 
1.1%
2119
 
1.0%
2309
 
1.0%
2239
 
1.0%
2199
 
1.0%
2169
 
1.0%
2049
 
1.0%
2608
 
0.9%
Other values (207)636
69.1%
(Missing)30
 
3.3%
ValueCountFrequency (%)
0172
18.7%
851
 
0.1%
1002
 
0.2%
1171
 
0.1%
1261
 
0.1%
1291
 
0.1%
1311
 
0.1%
1321
 
0.1%
1391
 
0.1%
1411
 
0.1%
ValueCountFrequency (%)
6031
0.1%
5641
0.1%
5291
0.1%
5181
0.1%
4911
0.1%
4681
0.1%
4661
0.1%
4581
0.1%
4171
0.1%
4121
0.1%

fbs
Boolean

Missing 

Distinct2
Distinct (%)0.2%
Missing90
Missing (%)9.8%
Memory size7.3 KiB
False
692 
True
138 
(Missing)
90 
ValueCountFrequency (%)
False692
75.2%
True138
 
15.0%
(Missing)90
 
9.8%
2025-10-24T15:52:14.909630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

restecg
Categorical

Distinct3
Distinct (%)0.3%
Missing2
Missing (%)0.2%
Memory size7.3 KiB
normal
551 
lv hypertrophy
188 
st-t abnormality
179 

Length

Max length16
Median length6
Mean length9.5882353
Min length6

Characters and Unicode

Total characters8,802
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlv hypertrophy
2nd rowlv hypertrophy
3rd rowlv hypertrophy
4th rownormal
5th rowlv hypertrophy

Common Values

ValueCountFrequency (%)
normal551
59.9%
lv hypertrophy188
 
20.4%
st-t abnormality179
 
19.5%
(Missing)2
 
0.2%

Length

2025-10-24T15:52:15.114824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T15:52:15.311641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
normal551
42.9%
lv188
 
14.6%
hypertrophy188
 
14.6%
st-t179
 
13.9%
abnormality179
 
13.9%

Most occurring characters

ValueCountFrequency (%)
r1106
12.6%
l918
10.4%
o918
10.4%
a909
10.3%
n730
8.3%
m730
8.3%
t725
8.2%
y555
 
6.3%
h376
 
4.3%
p376
 
4.3%
Other values (7)1459
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)8802
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r1106
12.6%
l918
10.4%
o918
10.4%
a909
10.3%
n730
8.3%
m730
8.3%
t725
8.2%
y555
 
6.3%
h376
 
4.3%
p376
 
4.3%
Other values (7)1459
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8802
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r1106
12.6%
l918
10.4%
o918
10.4%
a909
10.3%
n730
8.3%
m730
8.3%
t725
8.2%
y555
 
6.3%
h376
 
4.3%
p376
 
4.3%
Other values (7)1459
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8802
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r1106
12.6%
l918
10.4%
o918
10.4%
a909
10.3%
n730
8.3%
m730
8.3%
t725
8.2%
y555
 
6.3%
h376
 
4.3%
p376
 
4.3%
Other values (7)1459
16.6%

thalch
Real number (ℝ)

Missing 

Distinct119
Distinct (%)13.8%
Missing55
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean137.54566
Minimum60
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-10-24T15:52:15.529738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile95
Q1120
median140
Q3157
95-th percentile178
Maximum202
Range142
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.926276
Coefficient of variation (CV)0.18849214
Kurtosis-0.47972463
Mean137.54566
Median Absolute Deviation (MAD)20
Skewness-0.21111858
Sum118977
Variance672.17181
MonotonicityNot monotonic
2025-10-24T15:52:15.805644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15043
 
4.7%
14041
 
4.5%
12035
 
3.8%
13030
 
3.3%
16026
 
2.8%
11021
 
2.3%
17020
 
2.2%
12520
 
2.2%
12216
 
1.7%
14214
 
1.5%
Other values (109)599
65.1%
(Missing)55
 
6.0%
ValueCountFrequency (%)
601
0.1%
631
0.1%
671
0.1%
691
0.1%
701
0.1%
711
0.1%
722
0.2%
731
0.1%
771
0.1%
781
0.1%
ValueCountFrequency (%)
2021
 
0.1%
1951
 
0.1%
1941
 
0.1%
1921
 
0.1%
1902
0.2%
1882
0.2%
1871
 
0.1%
1862
0.2%
1854
0.4%
1844
0.4%

exang
Boolean

Missing 

Distinct2
Distinct (%)0.2%
Missing55
Missing (%)6.0%
Memory size7.3 KiB
False
528 
True
337 
(Missing)
55 
ValueCountFrequency (%)
False528
57.4%
True337
36.6%
(Missing)55
 
6.0%
2025-10-24T15:52:16.050463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

oldpeak
Real number (ℝ)

Missing  Zeros 

Distinct53
Distinct (%)6.2%
Missing62
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean0.87878788
Minimum-2.6
Maximum6.2
Zeros370
Zeros (%)40.2%
Negative12
Negative (%)1.3%
Memory size7.3 KiB
2025-10-24T15:52:16.273823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile0
Q10
median0.5
Q31.5
95-th percentile3
Maximum6.2
Range8.8
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.0912262
Coefficient of variation (CV)1.2417402
Kurtosis1.1270692
Mean0.87878788
Median Absolute Deviation (MAD)0.5
Skewness1.0414266
Sum754
Variance1.1907747
MonotonicityNot monotonic
2025-10-24T15:52:16.543506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0370
40.2%
183
 
9.0%
276
 
8.3%
1.548
 
5.2%
328
 
3.0%
0.519
 
2.1%
1.217
 
1.8%
2.516
 
1.7%
0.815
 
1.6%
1.415
 
1.6%
Other values (43)171
18.6%
(Missing)62
 
6.7%
ValueCountFrequency (%)
-2.61
0.1%
-21
0.1%
-1.51
0.1%
-1.11
0.1%
-12
0.2%
-0.91
0.1%
-0.81
0.1%
-0.71
0.1%
-0.52
0.2%
-0.11
0.1%
ValueCountFrequency (%)
6.21
 
0.1%
5.61
 
0.1%
51
 
0.1%
4.41
 
0.1%
4.22
 
0.2%
48
0.9%
3.81
 
0.1%
3.71
 
0.1%
3.64
0.4%
3.52
 
0.2%

slope
Categorical

Missing 

Distinct3
Distinct (%)0.5%
Missing309
Missing (%)33.6%
Memory size7.3 KiB
flat
345 
upsloping
203 
downsloping
63 

Length

Max length11
Median length4
Mean length6.3829787
Min length4

Characters and Unicode

Total characters3,900
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdownsloping
2nd rowflat
3rd rowflat
4th rowdownsloping
5th rowupsloping

Common Values

ValueCountFrequency (%)
flat345
37.5%
upsloping203
22.1%
downsloping63
 
6.8%
(Missing)309
33.6%

Length

2025-10-24T15:52:16.814493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T15:52:17.034045image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
flat345
56.5%
upsloping203
33.2%
downsloping63
 
10.3%

Most occurring characters

ValueCountFrequency (%)
l611
15.7%
p469
12.0%
f345
8.8%
t345
8.8%
a345
8.8%
n329
8.4%
o329
8.4%
i266
6.8%
s266
6.8%
g266
6.8%
Other values (3)329
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l611
15.7%
p469
12.0%
f345
8.8%
t345
8.8%
a345
8.8%
n329
8.4%
o329
8.4%
i266
6.8%
s266
6.8%
g266
6.8%
Other values (3)329
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l611
15.7%
p469
12.0%
f345
8.8%
t345
8.8%
a345
8.8%
n329
8.4%
o329
8.4%
i266
6.8%
s266
6.8%
g266
6.8%
Other values (3)329
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l611
15.7%
p469
12.0%
f345
8.8%
t345
8.8%
a345
8.8%
n329
8.4%
o329
8.4%
i266
6.8%
s266
6.8%
g266
6.8%
Other values (3)329
8.4%

ca
Categorical

Missing 

Distinct4
Distinct (%)1.3%
Missing611
Missing (%)66.4%
Memory size7.3 KiB
0.0
181 
1.0
67 
2.0
41 
3.0
20 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters927
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row3.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0181
 
19.7%
1.067
 
7.3%
2.041
 
4.5%
3.020
 
2.2%
(Missing)611
66.4%

Length

2025-10-24T15:52:17.254219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T15:52:17.454471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0181
58.6%
1.067
 
21.7%
2.041
 
13.3%
3.020
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0490
52.9%
.309
33.3%
167
 
7.2%
241
 
4.4%
320
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0490
52.9%
.309
33.3%
167
 
7.2%
241
 
4.4%
320
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0490
52.9%
.309
33.3%
167
 
7.2%
241
 
4.4%
320
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0490
52.9%
.309
33.3%
167
 
7.2%
241
 
4.4%
320
 
2.2%

thal
Categorical

Missing 

Distinct3
Distinct (%)0.7%
Missing486
Missing (%)52.8%
Memory size7.3 KiB
normal
196 
reversable defect
192 
fixed defect
46 

Length

Max length17
Median length12
Mean length11.502304
Min length6

Characters and Unicode

Total characters4,992
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfixed defect
2nd rownormal
3rd rowreversable defect
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal196
21.3%
reversable defect192
 
20.9%
fixed defect46
 
5.0%
(Missing)486
52.8%

Length

2025-10-24T15:52:17.674257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T15:52:17.884771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
defect238
35.4%
normal196
29.2%
reversable192
28.6%
fixed46
 
6.8%

Most occurring characters

ValueCountFrequency (%)
e1098
22.0%
r580
11.6%
l388
 
7.8%
a388
 
7.8%
d284
 
5.7%
f284
 
5.7%
t238
 
4.8%
c238
 
4.8%
238
 
4.8%
n196
 
3.9%
Other values (7)1060
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1098
22.0%
r580
11.6%
l388
 
7.8%
a388
 
7.8%
d284
 
5.7%
f284
 
5.7%
t238
 
4.8%
c238
 
4.8%
238
 
4.8%
n196
 
3.9%
Other values (7)1060
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1098
22.0%
r580
11.6%
l388
 
7.8%
a388
 
7.8%
d284
 
5.7%
f284
 
5.7%
t238
 
4.8%
c238
 
4.8%
238
 
4.8%
n196
 
3.9%
Other values (7)1060
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1098
22.0%
r580
11.6%
l388
 
7.8%
a388
 
7.8%
d284
 
5.7%
f284
 
5.7%
t238
 
4.8%
c238
 
4.8%
238
 
4.8%
n196
 
3.9%
Other values (7)1060
21.2%

num
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
0
411 
1
265 
2
109 
3
107 
4
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters920
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0411
44.7%
1265
28.8%
2109
 
11.8%
3107
 
11.6%
428
 
3.0%

Length

2025-10-24T15:52:18.104895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-24T15:52:18.293923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0411
44.7%
1265
28.8%
2109
 
11.8%
3107
 
11.6%
428
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0411
44.7%
1265
28.8%
2109
 
11.8%
3107
 
11.6%
428
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0411
44.7%
1265
28.8%
2109
 
11.8%
3107
 
11.6%
428
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0411
44.7%
1265
28.8%
2109
 
11.8%
3107
 
11.6%
428
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0411
44.7%
1265
28.8%
2109
 
11.8%
3107
 
11.6%
428
 
3.0%

Interactions

2025-10-24T15:52:08.675471image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:02.522883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:03.623934image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:05.155028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:06.357144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:07.506930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:08.848609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:02.703329image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:03.795071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:05.344031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:06.534618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:07.692998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:09.021334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:02.870077image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:03.963841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:05.529985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:06.717746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:07.875351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:09.222586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:03.077012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:04.166510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:05.744013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:06.927398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:08.093394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:09.408394image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:03.260844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:04.348814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:05.932743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:07.122612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:08.287957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:09.603989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:03.454544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:04.984096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:06.165460image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:07.330271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-24T15:52:08.492846image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-10-24T15:52:18.464000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
agecacholcpdatasetexangfbsidnumoldpeakrestecgsexslopethalthalchtrestbps
age1.0000.218-0.0370.1490.2570.1850.2190.2380.1620.2880.1620.0000.1200.130-0.3480.259
ca0.2181.0000.0670.1470.0860.1700.1260.0470.3300.1750.0780.0890.0780.1590.1210.036
chol-0.0370.0671.0000.1220.4940.0970.072-0.3080.1740.0480.1530.2080.0620.1450.1750.104
cp0.1490.1470.1221.0000.2040.4460.0650.2600.3070.1980.0870.1950.1850.2550.2210.044
dataset0.2570.0860.4940.2041.0000.2480.2830.8960.2980.2590.4380.2910.2940.2520.2530.098
exang0.1850.1700.0970.4460.2481.0000.0000.3570.4630.4410.0770.1750.3430.3370.3900.143
fbs0.2190.1260.0720.0650.2830.0001.0000.2830.1580.0270.1670.0780.0920.1310.0000.163
id0.2380.047-0.3080.2600.8960.3570.2831.0000.3380.0500.4390.3390.3040.275-0.4740.057
num0.1620.3300.1740.3070.2980.4630.1580.3381.0000.2660.1310.3020.2810.3500.2100.081
oldpeak0.2880.1750.0480.1980.2590.4410.0270.0500.2661.0000.1150.1180.3610.185-0.1880.161
restecg0.1620.0780.1530.0870.4380.0770.1670.4390.1310.1151.0000.0570.0660.1520.1160.078
sex0.0000.0890.2080.1950.2910.1750.0780.3390.3020.1180.0571.0000.1110.3750.1690.000
slope0.1200.0780.0620.1850.2940.3430.0920.3040.2810.3610.0660.1111.0000.2250.2960.087
thal0.1300.1590.1450.2550.2520.3370.1310.2750.3500.1850.1520.3750.2251.0000.2840.030
thalch-0.3480.1210.1750.2210.2530.3900.000-0.4740.210-0.1880.1160.1690.2960.2841.000-0.090
trestbps0.2590.0360.1040.0440.0980.1430.1630.0570.0810.1610.0780.0000.0870.030-0.0901.000

Missing values

2025-10-24T15:52:09.856806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-24T15:52:10.303436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-24T15:52:10.628089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idagesexdatasetcptrestbpscholfbsrestecgthalchexangoldpeakslopecathalnum
0163MaleClevelandtypical angina145.0233.0Truelv hypertrophy150.0False2.3downsloping0.0fixed defect0
1267MaleClevelandasymptomatic160.0286.0Falselv hypertrophy108.0True1.5flat3.0normal2
2367MaleClevelandasymptomatic120.0229.0Falselv hypertrophy129.0True2.6flat2.0reversable defect1
3437MaleClevelandnon-anginal130.0250.0Falsenormal187.0False3.5downsloping0.0normal0
4541FemaleClevelandatypical angina130.0204.0Falselv hypertrophy172.0False1.4upsloping0.0normal0
5656MaleClevelandatypical angina120.0236.0Falsenormal178.0False0.8upsloping0.0normal0
6762FemaleClevelandasymptomatic140.0268.0Falselv hypertrophy160.0False3.6downsloping2.0normal3
7857FemaleClevelandasymptomatic120.0354.0Falsenormal163.0True0.6upsloping0.0normal0
8963MaleClevelandasymptomatic130.0254.0Falselv hypertrophy147.0False1.4flat1.0reversable defect2
91053MaleClevelandasymptomatic140.0203.0Truelv hypertrophy155.0True3.1downsloping0.0reversable defect1
idagesexdatasetcptrestbpscholfbsrestecgthalchexangoldpeakslopecathalnum
91091151FemaleVA Long Beachasymptomatic114.0258.0Truelv hypertrophy96.0False1.0upslopingNaNNaN0
91191262MaleVA Long Beachasymptomatic160.0254.0Truest-t abnormality108.0True3.0flatNaNNaN4
91291353MaleVA Long Beachasymptomatic144.0300.0Truest-t abnormality128.0True1.5flatNaNNaN3
91391462MaleVA Long Beachasymptomatic158.0170.0Falsest-t abnormality138.0True0.0NaNNaNNaN1
91491546MaleVA Long Beachasymptomatic134.0310.0Falsenormal126.0False0.0NaNNaNnormal2
91591654FemaleVA Long Beachasymptomatic127.0333.0Truest-t abnormality154.0False0.0NaNNaNNaN1
91691762MaleVA Long Beachtypical anginaNaN139.0Falsest-t abnormalityNaNNaNNaNNaNNaNNaN0
91791855MaleVA Long Beachasymptomatic122.0223.0Truest-t abnormality100.0False0.0NaNNaNfixed defect2
91891958MaleVA Long BeachasymptomaticNaN385.0Truelv hypertrophyNaNNaNNaNNaNNaNNaN0
91992062MaleVA Long Beachatypical angina120.0254.0Falselv hypertrophy93.0True0.0NaNNaNNaN1